A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur,Malaysia

Kuala Lumpur of Malaysia,as a tropical city,has experienced a notable decline in its critical urban green infrastructure(UGI)due to rapid urbanization and haphazard development.The decrease of UGI,especially natural forest and artificial forest,may reduce the diversity of ecosystem services and the ability of Kuala Lumpur to build resilience in the future.This study analyzed land use and land cover(LULC)and UGI changes in Kuala Lumpur based on Landsat satellite images in 1990,2005,and 2021and employed the overall accuracy and Kappa coefficient to assess classification accuracy.LULC was categorized into six main types:natural forest,artificial forest,grassland,water body,bare ground,and built-up area.Satellite images in 1990,2005,and 2021 showed the remarkable overall accuracy values of 91.06%,96.67%,and 98.28%,respectively,along with the significant Kappa coefficient values of 0.8997,0.9626,and 0.9512,respectively.Then,this study utilized Cellular Automata and Markov Chain model to analyze the transition of different LULC types during 1990-2005 and 1990-2021 and predict LULC types in 2050.The results showed that natural forest decreased from 15.22%to 8.20%and artificial forest reduced from 18.51%to 15.16%during 1990-2021.Reductions in natural forest and artificial forest led to alterations in urban surface water dynamics,increasing the risk of urban floods.However,grassland showed a significant increase from 7.80%to 24.30%during 1990-2021.Meanwhile,bare ground increased from 27.16%to 31.56%and built-up area increased from 30.45%to 39.90%during 1990-2005.In 2021,built-up area decreased to 35.10%and bare ground decreased to 13.08%,indicating a consistent dominance of built-up area in the central parts of Kuala Lumpur.This study highlights the importance of integrating past,current,and future LULC changes to improve urban ecosystem services in the city.

An Improved Markov Chain Model Based on Autocorrelation and Entropy Techniques and Its Application to State Prediction of Water Resources

According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.

Testing and Evaluation for Web Usability Based on Extended Markov Chain Model

As the increasing popularity and complexity of Web applications and the emergence of their new characteristics, the testing and maintenance of large, complex Web applications are becoming more complex and difficult. Web applications generally contain lots of pages and are used by enormous users. Statistical testing is an effective way of ensuring their quality. Web usage can be accurately described by Markov chain which has been proved to be an ideal model for software statistical testing. The results of unit testing can be utilized in the latter stages, which is an important strategy for bottom-to-top integration testing, and the other improvement of extended Markov chain model (EMM) is to present the error type vector which is treated as a part of page node. this paper also proposes the algorithm for generating test cases of usage paths. Finally, optional usage reliability evaluation methods and an incremental usability regression testing model for testing and evaluation are presented. Key words statistical testing - evaluation for Web usability - extended Markov chain model (EMM) - Web log mining - reliability evaluation CLC number TP311. 5 Foundation item: Supported by the National Defence Research Project (No. 41315. 9. 2) and National Science and Technology Plan (2001BA102A04-02-03)Biography: MAO Cheng-ying (1978-), male, Ph.D. candidate, research direction: software testing. Research direction: advanced database system, software testing, component technology and data mining.

Analysis of Supply Chain Model of Agricultural Products and Quality Safety——A Case Study of Heilongjiang Province

This paper analyses the supply chain models of four types of agricultural products,namely fruits and vegetables,poultry,aquatic products and dairy,and the food safety problems arising from the links of supply chain.In view of different models,corresponding suggestions are put forward to ensure the quality safety of agricultural products in Heilongjiang Province.

Compressed wormlike chain moving out of confined space: A model of DNA ejection from bacteriophage

The molecular biomechanics of DNA ejection from bacteriophage is of interest to not only fundamental biological understandings but also practical applications such as the design of advanced site-specific and controllable drug delivery systems. In this paper, we analyze the viscous motion of a semiflexible polymer chain coming out of a strongly confined space as a model to investigate the effects of various structure confinements and frictional resistances encountered during the DNA ejection process. The theoretically predicted relations between the ejection speed, ejection time, ejection length, and other physical parameters, such as the phage type, total genome length and ionic state of external buffer solutions, show excellent agreement with in vitro experimental observations in the literature.

CONSIDER SAINT-VENANT'S PRINCIPLE BY MEANS OF CHAIN MODEL

A precise background theory of computational mechanics is formed. Saint_Venant's principle is discussed in chain model by means of this precise theory. The classical continued fraction is developed into operator continued fraction to be the constrictive formulation of the chain model. The decay of effect of a self_equilibrated system of forces in chain model is decided by the convergence of operator continued fraction, so the reasonable part of Saint_Venant's principle is described as the convergence of operator continued fraction. In case of divergence the effect of a self_equilibrated system of forces may be non_zero at even infinite distant sections, so Saint_Venant's principle is not a common principle.

Deterministic and Stochastic Fractional-Order Hastings-Powell Food Chain Model

In this paper,a deterministic and stochastic fractional-order model of the tri-trophic food chain model incorporating harvesting is proposed and analysed.The interaction between prey,middle predator and top predator population is investigated.In order to clarify the characteristics of the proposed model,the analysis of existence,uniqueness,non-negativity and boundedness of the solutions of the proposed model are examined.Some sufficient conditions that ensure the local and global stability of equilibrium points are obtained.By using stability analysis of the fractional-order system,it is proved that if the basic reproduction number R_(0)<1,the predator free equilibrium point E_(1) is globally asymptotically stable.The occurrence of local bifurcation near the equilibrium points is investigated with the help of Sotomayor’s theorem.Some numerical examples are given to illustrate the theoretical findings.The impact of harvesting on prey and themiddle predator is studied.We conclude that harvesting parameters can control the dynamics of the middle predator.A numerical approximation method is developed for the proposed stochastic fractional-order model.

Mathematical modelling of axonal microtubule bundles under dynamic torsion

Owing to its viscoelastic nature, axon exhibits a stress rate-dependent me- chanical behavior. An extended tension-shear chain model with Kelvin-Voigt viscoelas- ticity is developed to illustrate the micromechanical behavior of the axon under dynamic torsional conditions. Theoretical closed-form expressions are derived to predict the de- formation, stress transfer, and failure mechanism between microtubule （MT） and tau protein while the axon is sheared dynamically. The results obtained from the present an- alytical solutions demonstrate how the MT-tau interface length, spacing between the tau proteins, and loading rate affect the mechanical properties of axon. Moreover, it is found that the MTs are more prone to rupture due to the contributions from the viscoelastic effects. Under the torsional force, the MTs are so long that the stress concentrates at the loaded end where axonal MTs will break. This MT-tau protein dynamics model can help to understand the underlying pathology and molecular mechanisms of axonal injury. Additionally, the emphasis of this paper is on the micromechanical behavior of axon, whereas this theoretical model can be equally applicable to other soft or hard tissues, owning the similar fibrous structure.

One-dimensional chain of quantum molecule motors as a mathematical physics model for muscle fibers

A quantum chain model of multiple molecule motors is proposed as a mathematical physics theory for the microscopic modeling of classical force-velocity relation and tension transients in muscle fibers. The proposed model was a quantum many-particle Hamiltonian to predict the force-velocity relation for the slow release of muscle fibers, which has not yet been empirically defined and was much more complicated than the hyperbolic relationships. Using the same Hamiltonian model, a mathematical force-velocity relationship was proposed to explain the tension observed when the muscle was stimulated with an alternative electric current. The discrepancy between input electric frequency and the muscle oscillation frequency could be explained physically by the Doppler effect in this quantum chain model. Further more, quantum physics phenomena were applied to explore the tension time course of cardiac muscle and insect flight muscle. Most of the experimental tension transient curves were found to correspond to the theoretical output of quantum two- and three-level models. Mathematical modeling electric stimulus as photons exciting a quantum three-level particle reproduced most of the tension transient curves of water bug Lethocerus maximus.

Option Pricing and Hedging under a Markov Switching Lévy Process Model

In this paper, we consider a Markov switching Lévy process model in which the underlying risky assets are driven by the stochastic exponential of Markov switching Lévy process and then apply the model to option pricing and hedging. In this model, the market interest rate, the volatility of the underlying risky assets and the N-state compensator,depend on unobservable states of the economy which are modeled by a continuous-time Hidden Markov process. We use the MEMM(minimal entropy martingale measure) as the equivalent martingale measure. The option price using this model is obtained by the Fourier transform method. We obtain a closed-form solution for the hedge ratio by applying the local risk minimizing hedging.

Aharonov-Anandan Phases in Lipkin-Meskov-Glick Model

In the system of several interacting spins,geometric phases have been researched intensively.However,the studies are mainly focused on the adiabatic case (Berry phase),so it is necessary for us to study the non-adiabaticcounterpart (Aharonov and Anandan phase).In this paper,we analyze both the non-degenerate and degenerate geometricphase of Lipkin-Meskov-Glick type model,which has many application in Bose-Einstein condensates and entanglementtheory.Furthermore,in order to calculate degenerate geometric phases,the Floquet theorem and decomposition ofoperator are generalized.And the general formula is achieved.

A DYNAMIC FOOD CHAIN MODEL FOR HONG KONG BASED ON RADFOOD MODEL AND BIRCHALL－JAMES ALGORITHM

In this paper a dynamic food chain model for Hong Kong which simulates the transfer of radioactive substances from a fallout deposition via the food chain into the human bodies is built. The model is based on the RADFOOD model and the BirchallJames algorithm. The radionuclides 13if and 90Sr representing the short-term and long-term risk situations have been studied as sample cases. Various types of crops,and the dietary pattern of the public have been collsidered. The resulting internal radiation doses have been calculated. The results are obtained for food consumption starting at various time after the fallout deposition and for different consumption durations.

Softw are Maintainability Evaluation Based on Quantitive Model

Software maintainability is one of the most important factors of software quality,but it is seriously difficult to evaluate the maintainability. Without evaluation,it is impossible to control. To estimate software maintainability state,parameter system of software was built up and maintainability state was defined into three states.Thought of application on maintainability evaluation based on hidden Markov chain( HMC) and fuzzy inference was presented.Three-state maintainability estimation model was constructed. To testify the feasibility of the model, a real example of software maintenance activity was carried out and the result from the example validated that the results of this study were applicable.

Characterization and Modeling Home Power Grid Topologies in PLC Broadband

Home power outlets can be connected in various topologies： derivation, star, cascade. This paper firstly describes the intrinsic behavior of the transfer function of these basic topologies. Secondly, the channel capacities of the three topologies are compared to check if there is a more favorable topology. Many deterministic modeling methods have been developed to compute the transfer function of power line networks but the given examples in the studies correspond only to the derivation topology with branches connected to the direct path between transceivers. Thirdly, this paper evaluates the ability of common modeling methods （multipath and chain matrices） to compute accurately the transfer function of any topology. Modeling the derivation topology with ＂secondary＂ branches and the star topology is shown to be inappropriate with chain matrices based method. Indeed, this method is very sensitive to the uncertainty of the second parameters of the power cables and this induces considerable fading shifts for those topologies. Multipath modeling method produces results agreeing with measurements for any topology.

The Research of Supply Chain Management Resource Model

Considering the harder and harder competition among enterprises, this paper puts forward the Resource model of supply chain （RBV） to enhance competitive advantages, then analyses the source of supply chain competitive advantages and introduce the advantage formula. Finally, the rent contribution to supply chain management is explained in detail.

An Improved Inventory Coordination Model in a Multi-stage Multi-customer Supply Chain

To solve the inventory coordination model in a multi-stage, multi-customer supply chain, this paper first analyzes the third model （integer powers of two multipliers at each firm） studied by Moutaz Khouja （2003）, and the authors take a numerical example to prove that the third model is irrational to miss feasible solution. Then this paper puts up a new improved model （integer multiplier at each firm）, and takes the example to prove it gives better results than the integer powers of two multipliers at each firm.

Research on Model of Virtual Organization and Its Practice

This paper discusses an organizational model to be used for both conventional and virtual organizations. The model deals with variable relationships within an organization and provides a framework for overall organizational design that may include relationship among different design variables and external relationship with environment. Based on the researches of virtual organization, this paper also illustrates the new model of organization in the real world such as Beijing 2008 Olympic games and Dongfeng Automobile group.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.